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In recent years, the topic of embedded machine learning has become very popular in AI research. With the help of various compression techniques such as pruning, quantization and others compression techniques, it became possible to run neural networks on embedded devices. These techniques have opened up a whole new application area for machine learning. They range from smart products such as voice assistants to smart sensors that are needed in robotics. Despite the achievements in embedded machine learning, efficient algorithms for training neural networks in constrained domains are still lacking. Training on embedded devices will open up further fields of applications. Efficient training algorithms would enable federated learning on embedded devices, in which the data remains where it was collected, or retraining of neural networks in different domains. In this paper, we summarize techniques that make training on embedded devices possible. We first describe the need and requirements for such algorithms. Then we examine existing techniques that address training in resource-constrained environments as well as techniques that are also suitable for training on embedded devices, such as incremental learning. At the end, we also discuss which problems and open questions still need to be solved in these areas.
Nowadays decarbonisation of the energy system is one of the main concerns for most governments. Renewable energy technologies, such as rooftop photovoltaic systems and home battery storage systems, are changing the energy system to be more decentralised. As a consequence, new ways of energy business models are emerging, e.g., peer-to-peer energy trading. This new concept provides an online marketplace where direct energy exchange can occur between its participants. The purpose of this study is to conduct a content analysis of the existing literature, ongoing research projects, and companies related to peer-to-peer energy trading. From this review, a summary of the most important aspects and journal papers is assessed, discussed, and classified. It was found that the different energy market types were named in various ways and a proposal for standard language for the several peer-to-peer market types and the different actors involved is suggested. Additionally, by grouping the most important attributes from peer-to-peer energy trading projects, an assessment of the entry barrier and scalability potential is performed by using a characterisation matrix.
Featherweight Generic Go (FGG) is a minimal core calculus modeling the essential features of the programming language Go. It includes support for overloaded methods, interface types, structural subtyping and generics. The most straightforward semantic description of the dynamic behavior of FGG programs is to resolve method calls based on runtime type information of the receiver.
This article shows a different approach by defining a type-directed translation from FGG to an untyped lambda-calculus. The translation of an FGG program provides evidence for the availability of methods as additional dictionary parameters, similar to the dictionary-passing approach known from Haskell type classes. Then, method calls can be resolved by a simple lookup of the method definition in the dictionary.
Every program in the image of the translation has the same dynamic semantics as its source FGG program. The proof of this result is based on a syntactic, step-indexed logical relation. The step-index ensures a well-founded definition of the relation in the presence of recursive interface types and recursive methods.
Recent advances in spiked shoe design, characterized by increased longitudinal stiffness, thicker midsole foams, and reconfigured geometry are considered to improve sprint performance. However, so far there is no empirical data on the effects of advanced spikes technology on maximal sprinting speed (MSS) published yet. Consequently, we assessed MSS via ‘flying 30m’ sprints of 44 trained male (PR: 10.32 s - 12.08 s) and female (PR: 11.56 s - 14.18 s) athletes, wearing both traditional and advanced spikes in a randomized, repeated measures design. The results revealed a statistically significant increase in MSS by 1.21% on average when using advanced spikes technology. Notably, 87% of participants showed improved MSS with the use of advanced spikes. A cluster analysis unveiled that athletes with higher MSS may benefit to a greater extent. However, individual responses varied widely, suggesting the influence of multiple factors that need detailed exploration. Therefore, coaches and athletes are advised to interpret the promising performance enhancements cautiously and evaluate the appropriateness of the advanced spike technology for their athletes critically.
Alexander von Humboldt, a German scientist and explorer of the 19th century, viewed the natural world holistically and described the harmony of nature among the diversity of the physical world as a conjoining between all physical disciplines. He noted in his diary: “Everything is interconnectedness.”
The main feature of Humboldt’s pioneering work was later named “Humboldtian science”, meaning the accurate study of interconnected real phenomena in order to find a definite law and a dynamic cause.
Following Humboldt's idea of nature, an Internet edition of his works must preserve the author’s original intention, retain an awareness of all relevant works, and still adhere to the requirements of scholarly edition.
At the present time, however, the highly unconventional form of his publications has undermined the awareness and a comprehensive study of Humboldt’s works.
Digital libraries should supply dynamic links to sources, maps, images, graphs and relevant texts. New forms of interaction and synthesis between humanistic texts and scientific observation need to be created.
Information technology is the only way to do justice to the broad range of visions, descriptions and the idea of nature of Humboldt’s legacy. It finally leads to virtual research environments as an adequate concept to redesign our digital archives, not only for Humboldt’s documents, but for all interconnected data.
Due to its performance, the field of deep learning has gained a lot of attention, with neural networks succeeding in areas like Computer Vision (CV), Neural Language Processing (NLP), and Reinforcement Learning (RL). However, high accuracy comes at a computational cost as larger networks require longer training time and no longer fit onto a single GPU. To reduce training costs, researchers are looking into the dynamics of different optimizers, in order to find ways to make training more efficient. Resource requirements can be limited by reducing model size during training or designing more efficient models that improve accuracy without increasing network size.
This thesis combines eigenvalue computation and high-dimensional loss surface visualization to study different optimizers and deep neural network models. Eigenvectors of different eigenvalues are computed, and the loss landscape and optimizer trajectory are projected onto the plane spanned by those eigenvectors. A new parallelization method for the stochastic Lanczos method is introduced, resulting in faster computation and thus enabling high-resolution videos of the trajectory and secondorder information during neural network training. Additionally, the thesis presents the loss landscape between two minima along with the eigenvalue density spectrum at intermediate points for the first time.
Secondly, this thesis presents a regularization method for Generative Adversarial Networks (GANs) that uses second-order information. The gradient during training is modified by subtracting the eigenvector direction of the biggest eigenvalue, preventing the network from falling into the steepest minima and avoiding mode collapse. The thesis also shows the full eigenvalue density spectra of GANs during training.
Thirdly, this thesis introduces ProxSGD, a proximal algorithm for neural network training that guarantees convergence to a stationary point and unifies multiple popular optimizers. Proximal gradients are used to find a closed-form solution to the problem of training neural networks with smooth and non-smooth regularizations, resulting in better sparsity and more efficient optimization. Experiments show that ProxSGD can find sparser networks while reaching the same accuracy as popular optimizers.
Lastly, this thesis unifies sparsity and neural architecture search (NAS) through the framework of group sparsity. Group sparsity is achieved through ℓ2,1-regularization during training, allowing for filter and operation pruning to reduce model size with minimal sacrifice in accuracy. By grouping multiple operations together, group sparsity can be used for NAS as well. This approach is shown to be more robust while still achieving competitive accuracies compared to state-of-the-art methods
This paper has the objective of creating a framework for a different cultural dimension of corporate entrepreneurship leading to corporate entrepreneurial culture (CEC). The analysis of CEC is based on a review of existing concepts of organisational culture and entrepreneurship. They are combined to create a framework of CEC, including macro- and microlevels and examples of subcultures. Core ideas of the framework are validated by qualitative interviews with ten experts. The identified organisational category of the CEC framework is defined by the levels of micro-cultures or subcultures and includes the upper levels of the hierarchy, including the industry level. Geographic categories such as regional or national culture are also part of the system. The individual category of the CEC framework is characterised by competencies (including aspects such as motivation, creativity, mobilising others, coping with uncertainty, teamwork and social competencies) and entrepreneurial personalities. The results of the interviews show the importance of these individual competencies for a lively CEC. The different levels, such as national and professional cultures, as a dimension of the organisational category of the framework are also confirmed by the interviews. The findings indicate that the individual category of CEC could be used for job satisfaction or engagement and the degree of CEC of an organisation could be defined and developed by the organisational category. The identified framework contributes to an understanding of this complex topic and supports companies in the implementation of entrepreneurial ideas in different organisational contexts.
Auswirkung eines Importstopps russischer Energieträger auf die Klimaschutzziele in Deutschland
(2022)
Ein Importstopp russischer Energieträger nach Deutschland wird derzeit vermehrt diskutiert. Wir wollen die Diskussion unterstützen, indem wir einen Weg zeigen, wie das Elektrizitätssystem in Deutschland kurzfristig mit geringen Energieimporten auskommt und welche Maßnahmen notwendig sind, um die Klimaschutzziele trotzdem einzuhalten. Die Ergebnisse eines solchen Energiewendeszenarios mit reduzierter Importabhängigkeit werden mit dem Energiesystemmodell MyPyPSA-Ger berechnet. Die wichtigsten Erkenntnisse sind, dass ein zügiger Ausbau Erneuerbarer Energien und von Speichertechnologien • die Abhängigkeit des deutschen Elektrizitätssystems von Energieimporten deutlich reduziert. • auch langfristig keine wesentlichen Importe der Energieträger Erdgas, Steinkohle und Mineralöl nach sich zieht. • über die Klimaziele der Bundesregierung hinaus das 1,5-Grad-Ziel im Elektrizitätssystem erreicht wird.
Automatic Identification of Travel Locations in Rare Books - Object Oriented Information Management
(2017)
The digital content of the Internet is growing exponentially and mass digitization of printed media opens access to literature, in particular the genre of travel literature from the 18th and 19th century, which consists of diaries or travel books describing routes, observations or inspirations. The identification of described locations in the digital text is a long-standing challenge which requires information technology to supply dynamic links to sources by new forms of interaction and synthesis between humanistic texts and scientific observations.
Using object oriented information technology, a prototype of a software tool is developed which makes it possible to automatically identify geographic locations and travel routes mentioned in rare books. The information objects contain properties such as names and classification codes for populated places, streams, mountains and regions. Together, with the latitudes and longitudes of every single location, it is possible to geo-reference this information in order that all processed and filtered datasets can be displayed by a map application. This method has already been used in the Humboldt Digital Library to present Alexander von Humboldt’s maps and was tested in a case study to prove the correctness and reliability of the automatic identification of locations based on the work of Alexander von Humboldt and Johann Wolfgang von Goethe.
The results reveal numerous errors due to misspellings, change of location names, equality of terms and location names. But on the other hand it becomes very clear that results of the automatic object detection and recognition can be improved by error-free and comprehensive sources. As a result an increase in quality and usability of the service can be expected, accompanied by more options to detect unknown locations in the descriptions of rare books.
We have developed a methodology for the systematic generation of a large image dataset of macerated wood references, which we used to generate image data for nine hardwood genera. This is the basis for a substantial approach to automate, for the first time, the identification of hardwood species in microscopic images of fibrous materials by deep learning. Our methodology includes a flexible pipeline for easy annotation of vessel elements. We compare the performance of different neural network architectures and hyperparameters. Our proposed method performs similarly well to human experts. In the future, this will improve controls on global wood fiber product flows to protect forests.
In this paper, we study the runtime performance of symmetric cryptographic algorithms on an embedded ARM Cortex-M4 platform. Symmetric cryptographic algorithms can serve to protect the integrity and optionally, if supported by the algorithm, the confidentiality of data. A broad range of well-established algorithms exists, where the different algorithms typically have different properties and come with different computational complexity. On deeply embedded systems, the overhead imposed by cryptographic operations may be significant. We execute the algorithms AES-GCM, ChaCha20-Poly1305, HMAC-SHA256, KMAC, and SipHash on an STM32 embedded microcontroller and benchmark the execution times of the algorithms as a function of the input lengths.
The variable refrigerant flow system is one of the best heating, ventilation, and air conditioning systems (HVAC) thanks to its ability to provide thermal comfort inside buildings. But, at the same time, these systems are considered one of the most energy-consuming systems in the building sector. Thus, it is crucial to well size the system according to the building’s cooling and heating needs and the indoor temperature fluctuations. Although many researchers have studied the optimization of the building energy performance considering heating or cooling needs, using air handling units, radiant floor heating, and direct expansion valves, few studies have considered the use of multi-objective optimization using only the thermostat setpoints of VRF systems for both cooling and heating needs. Thus, the main aim of this study is to conduct a sensitivity analysis and a multi-objective optimization strategy for a residential building containing a variable refrigerant flow system, to evaluate the effect of the building performance on energy consumption and improve the building energy efficiency. The numerical model was based on the EnergyPlus, jEPlus, and jEPlus+EA simulation engines. The approach used in this paper has allowed us to reach significant quantitative energy saving by varying the cooling and heating setpoints and scheduling scenarios. It should be stressed that this approach could be applied to several HVAC systems to reduce energy-building consumption.
CNN-based deep learning models for disease detection have become popular recently. We compared the binary classification performance of eight prominent deep learning models: DenseNet 121, DenseNet 169, DenseNet 201, EffecientNet b0, EffecientNet lite4, GoogleNet, MobileNet, and ResNet18 for their binary classification performance on combined Pulmonary Chest Xrays dataset. Despite the widespread application in different fields in medical images, there remains a knowledge gap in determining their relative performance when applied to the same dataset, a gap this study aimed to address. The dataset combined Shenzhen, China (CH) and Montgomery, USA (MC) data. We trained our model for binary classification, calculated different parameters of the mentioned models, and compared them. The models were trained to keep in mind all following the same training parameters to maintain a controlled comparison environment. End of the study, we found a distinct difference in performance among the other models when applied to the pulmonary chest Xray image dataset, where DenseNet169 performed with 89.38 percent and MobileNet with 92.2 percent precision.
Robust scheduling problem is a major decision problem that is addressed in the literature, especially for remanufacturing systems; this problem is complex because of the high uncertainty and complex constraints involved. Generally, the existing approaches are dedicated to specific processes and do not enable the quick and efficient generation and evaluation of schedules. With the emergence of the Industry 4.0 paradigm, data availability is now considered an opportunity to facilitate the decision-making process. In this study, a data-driven decisionmaking process is proposed to treat the robust scheduling problem of remanufacturing systems in uncertain environments. In particular, this process generates simulation models based on a data-driven modeling approach. A robustness evaluation approach is proposed to answer several decision questions. An application of the decision process in an industrial case of a remanufacturing system is presented herein, illustrating the impact of robustness evaluation results on real-life decisions.
DE\GLOBALIZE
(2022)
The artistic research cycle DE\GLOBALIZE is a media ecological search movement for the terrestrial. After examining matters of fact in India (2014-18), matters of concern in Egypt (2016-2019) and matters of care in the Upper Rhine (2018-22), the focus turns toward matters of violence in the Congo (2022). From matter to mater, mother-earth, the garden to exploitation. From science, water and climate to migration, oppression and extermination.
The long-term research is accessible through interactive web documentation. The platform serves as a continuous media-archaeological archive for a speculative ethnography. The relational structure of the videographic essay is enabling the forensic processing of single documents in the sense of the actor-network theory.
The subject of the presentation at IFM is a field trip to the Congo planned for March 2022, which will focus on the ambivalence of violence and care in collaboration with local artists. The field trip is based on the postcolonial reflection luderitzcargo by the author from 1996, in which a freight container was transformed into a translocal cinema in Namibia.
Through the journey to Congo, a group of media artists, a psychotherapist, a theater dramaturg, a filmmaker and a philosopher intend to explore the political, technological and psycho-geographic borders. By artistic interventions with locals, we want to interfere with relational string figures as part of the new Earth Politics. They are focusing on the displaced consumption of resources which are hard-fought and guarantee prosperity in the global north. The so-called ghost acreages are repressed and justified as part of a civilizational mission. With this trip, we want to confront our self-lies with the ones of our hosts. We want to confront ourselves with the foreign, the dark and the displaced ghosts within ourselves. In the presentation at the #IFM2022 Conference, the platform DE\GLOBALIZE will be problematized itself as an example of epistemic violence for the ethnographic memory of (Western) knowledge.
We are not the missionaries but the perplexed travellers. In our search movement, we are dealing with psychoanalysis, video, performance and trance. As disoriented white men we try the reversal of Black Skin and White Mask by Franz Fanon without blackfacing. We will not only care about the sensitivity of our skin but that of our g/hosts and the one of mother earth.
The identification of vulnerabilities is an important element in the software development life cycle to ensure the security of software. While vulnerability identification based on the source code is a well studied field, the identification of vulnerabilities on basis of a binary executable without the corresponding source code is more challenging. Recent research has shown, how such detection can be achieved by deep learning methods. However, that particular approach is limited to the identification of only 4 types of vulnerabilities. Subsequently, we analyze to what extent we could cover the identification of a larger variety of vulnerabilities. Therefore, a supervised deep learning approach using recurrent neural networks for the application of vulnerability detection based on binary executables is used. The underlying basis is a dataset with 50,651 samples of vulnerable code in the form of a standardized LLVM Intermediate Representation. The vectorised features of a Word2Vec model are used to train different variations of three basic architectures of recurrent neural networks (GRU, LSTM, SRNN). A binary classification was established for detecting the presence of an arbitrary vulnerability, and a multi-class model was trained for the identification of the exact vulnerability, which achieved an out-of-sample accuracy of 88% and 77%, respectively. Differences in the detection of different vulnerabilities were also observed, with non-vulnerable samples being detected with a particularly high precision of over 98%. Thus, the methodology presented allows an accurate detection of 23 (compared to 4) vulnerabilities.
Additive manufacturing enables the production of lightweight and resilient components with extensive design freedom. In the low-cost sector, material extrusion (e.g. Fused Deposition Modeling - FDM) has been the main method used to date. Thus, robust 3D printers and inexpensive 3D materials (polymer filaments) can be used. However, the printing times for FDM are very long and the quality of the dimensions and surfaces is limited. Recently, new processes from the field of Vat polymerization have entered the market. For example, masked stereolithography (mSLA) offers a significant improvement in component quality and build speed through the use of resins and large-area curing at still reasonable costs. Currently, there is only limited knowledge available on the optimal design of components using this young process. In this contribution, design guidelines are developed to determine the possibilities and limitations of mSLA from a design point of view. For this purpose, a number of test geometries are designed and investigated to obtain systematic insights into important design features, such as wall thickness, grooves and holes. In addition, typical problems in additive manufacturing, such as the design of overhangs and fits or the hollowing of components, are investigated. The evaluation of practical 3D printing tests thus provides important parameters that can be transferred to design guidelines of components for additive manufacturing using mSLA.
Socially assistive robots (SARs) are becoming more prevalent in everyday life, emphasizing the need to make them socially acceptable and aligned with users' expectations. Robots' appearance impacts users' behaviors and attitudes towards them. Therefore, product designers choose visual qualities to give the robot a character and to imply its functionality and personality. In this work, we sought to investigate the effect of cultural differences on Israeli and German designers' perceptions and preferences regarding the suitable visual qualities of SARs in four different contexts: a service robot for an assisted living/retirement residence facility, a medical assistant robot for a hospital environment, a COVID-19 officer robot, and a personal assistant robot for domestic use. Our results indicate that Israeli and German designers share similar perceptions of visual qualities and most of the robotics roles. However, we found differences in the perception of the COVID-19 officer robot's role and, by that, its most suitable visual design. This work indicates that context and culture play a role in users' perceptions and expectations; therefore, they should be taken into account when designing new SARs for diverse contexts.